FramePack is a next-frame prediction neural network structure that generates videos progressively using diffusion models.
The tool addresses the challenge of making video diffusion practical by compressing input contexts to a constant length, which keeps the generation workload invariant to video length. This approach allows the model to process very large numbers of frames efficiently. FramePack can generate a full minute of video at 30 frames per second using a 13-billion-parameter model on laptop GPUs with as little as 6GB of memory. The architecture enables training with batch sizes comparable to image diffusion models rather than the smaller batches typically required for video diffusion.
FramePack suits developers and researchers working on video generation tasks who need to run inference on consumer-grade hardware. The tool is designed as a functional desktop application with minimal dependencies and a standalone sampling system. It requires an Nvidia GPU from the RTX 30XX, 40XX, or 50XX series supporting fp16 and bf16 precision, running on Linux or Windows. Generation speed ranges from approximately 1.5 to 2.5 seconds per frame on high-end hardware, with slower speeds on laptop GPUs, though users receive visual feedback progressively as frames are generated.
The project maintains active engagement with its user base, with almost all open issues raised by outside adopters reporting real-world use cases. Maintainers typically respond to new issues and pull requests within a day, indicating responsive development practices.
